arXiv Machine Learning August 6, 2026 By Yixuan Florence Wu, Yilun Zhu, Naichen Shi

Multimodal Alignment Through Joint Kernel Entropic Gromov--Wasserstein Optimal Transport

Read the original on arXiv Machine Learning →

arXiv:2608. 04234v1 Announce Type: cross Abstract: We study the problem of aligning data from multiple modalities into a shared representation space, focusing on settings where strong pretrained unimodal encoders are available but cross-modal paired data are scarce.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Multimodal Representation Alignment for Cross-modal Information Retrieval

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